Three-dimensional (3D) scene modeling and generation have always been an important issue in the field of computer graphics, which have a wide range of application backgrounds, such as animation generation, film production, interior design and military simulation, etc. The application of 3D scene modeling and deformation technology to the field of interior design brings forth tremendous economic and social benefits to the current society and creates more speedy and convenient information along with more colorful spiritual enjoyment for people's lives. For example, people in the purchase of household goods hope to decorate to meet their own preferences for the interior style effect. It is somewhat difficult obviously for those who do not major in interior design to describe a specific interior style. But people may choose their favorite interior style in the network full of massive image data and generate a 3D scene of such style with the help of 3D scene modeling and deformation technology. The image-based 3D scene style modeling can not only meet the needs from the people more quickly, but also satisfy the various suggestions on revision put forward by the people more conveniently. The image-based 3D scene modeling is a basic and important issue in the fields of computer vision and computer graphics. At present, many scholars are committed to this field of research, for example, Xiao et al. from Hong Kong University of Science and Technology proposed modeling of street architecture in 2009, Xu et al. from National University of Defense Science and Technology proposed symmetric object modeling in 2011, and Nan et al. from Shenzhen Advanced Technology Research Institute of Chinese Academy of Sciences proposed indoor scene modeling in 2012. While image-based 3D scene modeling has made considerable progress, the present invention relates to the deformation of an indoor scene style which is modeling for style rather than modeling for scene, therefore conventional image-based 3D scene modeling cannot solve the problems relevant to the present invention.
In order to carry out the deformation of the indoor scene style, what firstly needs to be clarified in the present invention is the definition of indoor scene style. In the computer field, at present, yet no scholars have described or quantified the indoor scene style. According to a specialized advice from interior design professionals, the indoor scene style mainly includes two components, namely, the furniture layout in the scene and the color assortment for the scene. Thus the invention conducts the research on the indoor scene style from these two aspects. Yu et al. from UCLA proposed an algorithm for automatically placing furniture in a 3D scene in 2011, Merrell et al. from Stanford University proposed an interactive furniture placement system under guidance of interior design rules in 2011, and Fisher et al. from Stanford University proposed a sample-based object placement generation system in 2012. These layouts require a 3D indoor scene database used as a training set, and the construction of such database is both time-consuming and labor-intensive. In color learning, O' Donovan et al. from the University of Toronto proposed a color compatibility theory in 2011, giving an analysis of various parameters of the color in detail; Chen et al. from Beihang University proposed an edit propagation method based on structural feature preservation in 2012, which can perform color conversion on a target image according to a reference image so that the target image has a color feature consistent with the reference image. Although these work relates to the color conversion, but does not relate to the relation between color and the indoor scene style.
The above research status shows that at present none of the researches of indoor layout and color relates to the scene style, and the researches for the two aspects are not unified into a framework, but simply performed in respective aspects, meanwhile the construction for 3D database is not an easy task. Thereby, the present invention uses the massive image data on the network as a training set to learn the layout rules and color rules for indoor styles, and utilizes these rules to deform a given indoor 3D scene style so that the deformed 3D scene has a scene style similar to an input image.